VTT Research System
Not a member yet
161399 research outputs found
Sort by
Durable copper nanowires for flexible curvature sensors
No full textMetal nanowire-based flexible conducting surfaces (FCS) are vital for next-generation flexible and wearable sensors. Copper nanowires (CuNWs) offer a low-cost alternative to the expensive silver nanowires for fabricating FCS, yet their poor stability remains a significant challenge. In this study, we report the synthesis of ultralong CuNWs using a hydrothermal polyol method across a range of temperatures (120–180 ◦C). The CuNWs synthesised at 160 ◦C (CuNW-160) demonstrated the best performance. CuNW-160 films maintained stable conductivity for over 60 days in ambient conditions and thermal stability up to 140 ◦C. A capacitive curvature sensor was fabricated using FCS made with CuNW-160, which maintained consistent performance over 10,000 bending cycles and still showed good curvature sensitivity after 75 days. This highlights the potential use of the copper nanowires by tuning reaction temperature for use in reliable, low-cost flexible electronics
Deep spatio-temporal learning for multi-hazard events: A ConvGRU multi-label classification approach
No full textThe forecasting of multi-hazards is a vital, though underinvestigated, area of disaster risk management. The traditional studies have mainly focused on single-hazard forecasting, thus leaving its utility in real-world and realistic scenarios. This study, in turn, presents a spatio-temporal multi-label classification model, a framework designed expressly to capture the complex interrelationships between a range of hazards. The methodological framework used disaster occurrence data from the Open Federal Emergency Management Agency (OpenFEMA) database and converted the raw records of disasters into a multi-label dataset. Pressure-level reanalysis data is extracted from Climate Data Store (CDS) based on the multi-hazard event. Spatial data is extracted in 25 59 grid format in different temporal dependencies (12 h, 8 h, 6 h) at the 850 hPa pressure level. The model architecture combines convolutional neural networks (CNNs) with spatial attention mechanisms and gated recurrent units (GRUs) that model the temporal sequences. This combination enables multi-hazard predictions by utilizing the spatial and temporal data. Experimental analysis reveals that the proposed model outperformed the baseline variants, i.e., 2D CNN, Convolutional Long Short-Term Memory (ConvLSTM), and Convolutional Gated Recurrent Unit (ConvGRU) without attention. The proposed model achieved per-class accuracy up to 0.8868, the subset accuracy is 0.55, and the Hamming loss up to 0.127, which are 3.88%, 13.59% and 21.12% performance improvements over the baseline models respectively. In addition, the use of various lead times and the fusion of multiple lead times (12 h+8 h+6 h) significantly improves the predictive capability. The proposed framework has high potential for disaster preparedness and early warning systems in the real world. It proposes a flexible and efficient method of dealing with the growing complexity of multi-hazard environments
A Novel L<sub>1</sub>-and-L<sub>2</sub>-Norm-Integrated Parameter Identification Model for Robot Calibration
No full textRobots promote the social development by enhancing production efficiency, reducing labor costs, and improving service quality. Meanwhile, their application fields such as healthcare, transportation, and education can drive technological advancement and innovation, thus fostering economic growth and improving our quality of life. However, due to mechanical wear from prolonged operation, the robot absolute positioning error can reach several millimeters, thus it is impossible for them to perform precise tasks. To address this intractable problem, this study innovatively proposes a robot calibration method (FPSA) combining the search algorithm based on proportion integration differentiation (PID) controller and fuzzy strategy for adjusting hyperparameter, its novelties include: 1) designing a novel L1-and-L2-norm-integrated parameter identification model, then search algorithm [PID search algorithm (PSA)] based on PID controller is adopted to solve this model, which achieves the accurate identification of robot kinematic errors; and 2) developing an efficient fuzzy strategy for adjusting hyperparameter to achieve hyperparameter adaptation in the robot parameter identification model, thereby effectively enhancing the calibration computation efficiency and accuracy. Moreover, we conduct extensive calibration experiments on an HSR JR680 robot. These experimental results demonstrate that compared with other advanced calibration algorithms, the maximum error of the proposed FPSA calibration method is 9.57% higher than that of the most accurate identification model solved by PSA algorithm, which has single L2 norm regularization and objective function. Therefore, this research provides excellent calibration service for a robot, thus contributing to the prosperous development of application fields such as industrial manufacturing and smart agriculture.</p
Mechanical characterisation of ITER-specification tungsten using tensile and small punch testing methods
No full textTungsten is widely recognised for its exceptional properties. The material exhibits a high melting point, excellent thermal conductivity, and strong resistance to radiation damage, making it a key material for fusion energy applications.In this work, two types of mechanical tests were conducted on ITER-specification tungsten (A.L.M.T, Japan): tensile and small punch testing (SPT). Tensile testing provides fundamental properties such as yield strength, and ultimate tensile strength in specific rolling directions. SPT on the other hand, which is biaxial in nature, offers comparable data using miniature specimens, thus enabling efficient characterisation with limited material. The study aims to characterise the mechanical properties of the material and explore a correlation factor between the two approaches.Re-evaluation of the SPT correlation factors of ITER-specification tungsten in this study revealed noticeable deviation from the commonly adopted value in the European standard, with the yield strength factor showing the largest discrepancy. Furthermore, all correlation factors exhibited a noticeable temperature and directional (anisotropy) dependence, highlighting the importance of using material-specific values for accurate properties estimation
Small Modular Reactors in Denmark:A Technology and Cost Review
No full textThe Danish Energy Agency has asked Ea Energy Analyses and VTT Technical Research Centre of Finland to investigate the possibilities and consequences of integrating the compact nuclear reactor technology called Small Modular Reactor technologies (SMR technologies) into the Danish energy system. The project covers two subtasks: 1) a technical analysis of SMR technologies in a Danish context 2) and a system analysis that will assess the effects and value of integrating SMR into the Danish energy system. Thisreport constitutes the reporting of the first part
Assessment of soil impacts from lead release by lead-halide perovskite solar cells based on outdoor leaching tests
No full textPerovskite solar cells represent a promising technology in the photovoltaic industry due to their high power conversion efficiency, potential for cost-effective manufacturing and versatile applications. The most stable and efficient perovskites to date rely on lead (Pb), raising concerns about leaching into the environment; however Pb release so far has only been quantified under laboratory conditions, and no field-based assessment under real outdoor expsosure has yet evaluated this risk. The present study quantified Pb leaching from various metal-halide perovskite compositions, device stacks and encapsulation approaches in a rooftop installation for up to 9 months. Pb leaching was low across all tested configurations, even in intentionally damaged materials. Glass–glass encapsulated tandem devices shattered by hail and plastic-encapsulated samples damaged by 100 µm pinholes released only 0.07% ± 0.01% and 0.15% ± 0.14% of their initial Pb, respectively, likely due to the slow diffusion of Pb cations in water. The highest leaching (4.81% ± 0.02%) occurred in unlaminated laboratory devices, demonstrating the importance of proper lamination. A self-developed freeware web tool was used to calculate predicted soil concentrations and evaluate potential impacts. Even for unlaminated devices, concentrations would only slightly exceed natural background levels (5.6 mg kg−1 increase), with negligible effects on soil fertility. A hypothetical worst-case scenario assuming a 1000 nm thick perovskite layer and complete Pb leaching onto a narrow strip of soil predicted a negative impact on soil fertility; however remediation would still not be required under Swiss environmental regulations. Overall, current industry-standard encapsulation limits Pb leaching to levels that almost completely mitigate negative impacts on soil health.</p
Efficient debromination of tetrabromobisphenol A in protic solvents by supported nickel catalysts:Effect of metal-support interactions
No full textToxic and environmentally hazardous brominated flame retardants (BFR) hinder the recycling of plastic waste, which has led to the development of various extraction processes to remove them. These processes can be further advanced by debrominating BFRs into less harmful compounds with potential commercial value, thus supporting the principles of a circular economy. Herein, Ni/Al2O3 was employed for the catalytic debromination of tetrabromobisphenol A flame retardant in mixtures of H2O, isopropanol and NaOH at modest reaction temperatures. Activity experiments conducted in an autoclave indicated that studied catalyst exhibits impressive debromination activity and complete selectivity towards C−Br bond scission. The catalyst reduction temperature was found to correlate with debromination activity, with higher temperatures yielding improved performance. Debromination proceeded under H2 and also under N2 in protic solvents via transfer hydrogenation. Catalyst characterization, coupled with high-resolution mass-spectrometry product analytics and deuterium labelling, suggested that the enhanced catalytic activity can be attributed to the activation of the metal-support interface and subsequent interactions with adsorbed solvent molecules and associated dissociation products on the alumina support. Used experimental conditions also provided high tolerance against bromine poisoning of the catalyst, in contrast to reference debromination experiments conducted in toluene. The study demonstrates the capability of Ni/Al2O3 as an efficient and affordable debromination catalyst in solvents of low environmental impact. Furthermore, the results provide additional insights into structure-activity relationships of supported nickel catalysts in protic solvents, which can be leveraged for the development of more efficient and sustainable dehalogenation and heteroatom removal processes for environmental applications.</p
Global solar energy potential forecasting through machine learning and deep learning models
No full textClimate change is accelerating at an alarming rate, 2024 has been verified as the hottest year so far, with an average temperature of 1.55 °C warmer than upstream values set in the Paris Agreement. As such, extreme weather patterns like floods, hot weather, wild fires and glaciers melts that all pose a threat of harm to ecological systems. By investing in solar technology, nations can work towards a more sustainable energy future and addressing the pressing challenge of climate change. This study exploited the global solar photovoltaic (PV) energy potential using the Seasonal Autoregressive Integrated Moving Average with Exogenous Factors (SARIMAX) and Temporal Convolutional Network (TCN) models implemented in Python for the period 2023 to 2050 by taking input data from 2000 to 2022. The results revealed that, the solar PV capacity was 1.23 GW in the year 2000 which then increased to 1053.12 GW by 2022. SARIMAX and TCN models estimated the future of solar PV capacity which is increased from 1291.29 GW and 1094.40 GW in 2023 to 11641.41 GW and 11577.24 GW until 2050. However, the solar PV energy was 1.03 TWh in 2000 which then increased to 1323.32 TWh in 2022. SARIMAX and TCN models forecasted the future of solar PV energy which is increased from 1935.52 TWh and 1557.92 TWh in 2023 to 14967.15 TWh and 15928.52 TWh until 2050. It is observed from the results that SARIMAX model has higher accuracy as compared with the TCN model
Exploring the hydration potential and kinetics of Na-Ye'elimite and Ti-Ferrite solid solutions
Full text linkThe addition of Bauxite residue in the raw mix introduces Na+ and Ti4+ into the crystalline phases of calcium-sulfoaluminate (CSA) clinkers. To mimic such a system, Na-Fe-ye'elimite (C₃.₉N₀.₁A₂.₈F₀.₂Ŝ) and Ti-ferrite (C₂F₀.₇₆A₀.₂₄T₀.₁) were synthesized at 1285 °C, 2 h, and 1320 °C, 3 h, respectively. Quantitative X-ray diffraction (QXRD) revealed solid solutions with minor Ca-aluminates phases, whereas electron backscattered diffraction-energy dispersive spectroscopy (EBSD-EDS) could distinguish Na-rich orthorhombic and Fe-rich cubic ye'elimite polymorphs. Isothermal calorimetry showed the Na-Fe-ye'elimite phase drives early heat evolution, whereas higher ferrite and gypsum (M > 0) prolong induction and attenuate the main hydration peak. In ferrite-free mixes, the cubic-ye'elimite polymorph dissolves fastest, but when ferrite exceeds 33 wt%, its Fe3+ release accelerates orthorhombic-ye'elimite dissolution, as confirmed by pore-solution analysis. After 28d, Na-Fe-ye'elimite is fully consumed at M (sulfate to ye'elimite molar ratio) ≥ 2 for ye'elimite-ferrite mixes, while ferrite remains partly inert, possibly from Ca2+/SO₄2− adsorb onto its Fesingle bondAl surface. Limiting ferrite to ≤33 wt% is recommended to achieve more densification of the microstructure for better performance